Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?
Abstract: The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) feedback, CSI prediction, beam management, and positioning. In this paper, we set focus on the CSI prediction task and study the performance and generalization of the two main DL layers that are being extensively benchmarked within the DL community, namely, multi-head self-attention (MSA) and state-space model (SSM). We train and evaluate MSA and SSM layers to predict the next slot for uplink and downlink communication scenarios over urban microcell (UMi) and urban macrocell (UMa) OFDM 5G channel models. Our numerical results demonstrate that SSMs exhibit better prediction and generalization capabilities than MSAs only for SISO cases. For MIMO scenarios, however, the MSA layer outperforms the SSM one. While both layers represent potential DL architectures for future DL-enabled 5G use cases, the overall investigation of this paper favors MSAs over SSMs.
- J. Yang, H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, S. Zhong, B. Yin, and X. Hu, “Harnessing the power of llms in practice: A survey on chatgpt and beyond,” ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 6, pp. 1–32, 2024.
- L. Bariah, Q. Zhao, H. Zou, Y. Tian, F. Bader, and M. Debbah, “Large generative ai models for telecom: The next big thing?” IEEE Communications Magazine, 2024.
- X. Lin, “An overview of the 3gpp study on artificial intelligence for 5g new radio,” arXiv preprint arXiv:2308.05315, 2023.
- H. Kim, S. Kim, H. Lee, C. Jang, Y. Choi, and J. Choi, “Massive mimo channel prediction: Kalman filtering vs. machine learning,” IEEE Transactions on Communications, vol. 69, no. 1, pp. 518–528, 2020.
- J. B. Andersen, J. Jensen, S. H. Jensen, and F. Frederiksen, “Prediction of future fading based on past measurements,” in Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No. 99CH36324), vol. 1. IEEE, 1999, pp. 151–155.
- W. Peng, M. Zou, and T. Jiang, “Channel prediction in time-varying massive mimo environments,” IEEE Access, vol. 5, pp. 23 938–23 946, 2017.
- W. Jiang and H. D. Schotten, “Neural network-based fading channel prediction: A comprehensive overview,” IEEE Access, vol. 7, pp. 118 112–118 124, 2019.
- Y. Yang, F. Gao, Z. Zhong, B. Ai, and A. Alkhateeb, “Deep transfer learning-based downlink channel prediction for fdd massive mimo systems,” IEEE Transactions on Communications, vol. 68, no. 12, pp. 7485–7497, 2020.
- C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li, “Channel state information prediction for 5g wireless communications: A deep learning approach,” IEEE transactions on network science and engineering, vol. 7, no. 1, pp. 227–236, 2018.
- J. Wang, Y. Ding, S. Bian, Y. Peng, M. Liu, and G. Gui, “Ul-csi data driven deep learning for predicting dl-csi in cellular fdd systems,” IEEE Access, vol. 7, pp. 96 105–96 112, 2019.
- Y. Zhang, J. Wang, J. Sun, B. Adebisi, H. Gacanin, G. Gui, and F. Adachi, “Cv-3dcnn: Complex-valued deep learning for csi prediction in fdd massive mimo systems,” IEEE Wireless Communications Letters, vol. 10, no. 2, pp. 266–270, 2020.
- S. Mourya, P. Reddy, S. Amuru, and K. K. Kuchi, “Spectral temporal graph neural network for massive mimo csi prediction,” IEEE Wireless Communications Letters, 2024.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- H. Jiang, M. Cui, D. W. K. Ng, and L. Dai, “Accurate channel prediction based on transformer: Making mobility negligible,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 9, pp. 2717–2732, 2022.
- A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,” arXiv preprint arXiv:2312.00752, 2023.
- S. Jelassi, D. Brandfonbrener, S. M. Kakade, and E. Malach, “Repeat after me: Transformers are better than state space models at copying,” arXiv preprint arXiv:2402.01032, 2024.
- E. Nguyen, K. Goel, A. Gu, G. Downs, P. Shah, T. Dao, S. Baccus, and C. Ré, “S4nd: Modeling images and videos as multidimensional signals with state spaces,” Advances in neural information processing systems, vol. 35, pp. 2846–2861, 2022.
- M. M. Islam and G. Bertasius, “Long movie clip classification with state-space video models,” in European Conference on Computer Vision. Springer, 2022, pp. 87–104.
- C. Wang, O. Tsepa, J. Ma, and B. Wang, “Graph-mamba: Towards long-range graph sequence modeling with selective state spaces,” arXiv preprint arXiv:2402.00789, 2024.
- M. Akrout, A. Mezghani, E. Hossain, F. Bellili, and R. W. Heath, “From multilayer perceptron to gpt: A reflection on deep learning research for wireless physical layer,” arXiv preprint arXiv:2307.07359, 2023.
- M. Akrout, A. Feriani, F. Bellili, A. Mezghani, and E. Hossain, “Domain generalization in machine learning models for wireless communications: Concepts, state-of-the-art, and open issues,” IEEE Communications Surveys & Tutorials, 2023.
- Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, “Random erasing data augmentation,” in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 07, 2020, pp. 13 001–13 008.
- R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, “Improving robustness without sacrificing accuracy with patch gaussian augmentation,” arXiv preprint arXiv:1906.02611, 2019.
- C. M. Bishop, “Training with noise is equivalent to tikhonov regularization,” Neural computation, vol. 7, no. 1, pp. 108–116, 1995.
- A. Tustin, “A method of analysing the behaviour of linear systems in terms of time series,” Journal of the Institution of Electrical Engineers-Part IIA: Automatic Regulators and Servo Mechanisms, vol. 94, no. 1, pp. 130–142, 1947.
- A. Gu, K. Goel, and C. Ré, “Efficiently modeling long sequences with structured state spaces,” arXiv preprint arXiv:2111.00396, 2021.
- A. Gu, T. Dao, S. Ermon, A. Rudra, and C. Ré, “Hippo: Recurrent memory with optimal polynomial projections,” Advances in neural information processing systems, vol. 33, pp. 1474–1487, 2020.
- G. T. 38.901, “Study on channel model for frequencies from 0.5 to 100 ghz,” 2017.
- J. Hoydis, S. Cammerer, F. A. Aoudia, A. Vem, N. Binder, G. Marcus, and A. Keller, “Sionna: An open-source library for next-generation physical layer research,” arXiv preprint arXiv:2203.11854, 2022.
Summary
- The paper demonstrates that multi-head self-attention and state space models offer complementary strengths for predicting next-slot OFDM-CSI in diverse channel scenarios.
- The study reveals that MSA excels in capturing spatial correlations for MIMO setups, while SSM performs better in high-mobility SISO environments.
- Benchmarking under varied SNR and channel conditions provides critical insights for optimizing CSI prediction in evolving 5G networks.
Next-slot OFDM-CSI Prediction Using MSA and SSM Layers
Introduction
The Next-slot OFDM-CSI Prediction paper focuses on evaluating the performance of two deep learning architectures, Multi-head Self-attention (MSA) and State Space Model (SSM), for predicting Channel State Information (CSI) in OFDM systems as part of the 5G standardization process. These architectures are benchmarked due to their potential to enhance the effectiveness and efficiency of CSI prediction in both uplink and downlink communication scenarios.
Background
Both MSA and SSM layers have been thoroughly explored in artificial intelligence, particularly in natural language processing and control theory. The MSA layer uses attention mechanisms to learn correlations within sequences, making it apt for tasks requiring contextual understanding, while the SSM captures the system's dynamics via state equations, ideal for modeling time-series data.
Figure 1: CSI prediction mechanisms using state space models and multi-head attention layers.
OFDM-CSI Prediction Task
The task entails predicting the CSI for the next OFDM slot using previously observed CSI data within a 5G communication framework. This is crucial since CSI becomes outdated due to inherent delays in the reporting system. The prediction models need to adapt to dynamic channel environments, characterized by parameters such as SNR, user speed, carrier frequency, and channel type.
Figure 2: Illustration of the LTE radio frame structure.
Methodology
Each model (MSA and SSM) is trained with data generated from 3GPP-defined urban microcell (UMi) and urban macrocell (UMa) channel models, varying parameters such as user speeds (static to highly mobile) and SNR levels. Performance is compared under identical training and testing conditions (in-distribution) and varied conditions (out-of-distribution) to evaluate generalization capabilities.
Numerical Results
SISO Scenarios
For SISO communication, both MSA and SSM layers were assessed for their prediction accuracy:
- In-distribution performance: SSM outperforms MSA in scenarios with high user mobility, exhibiting better generalization in predicting rapidly changing channels.
- Impact of SNR diversification: Training on diverse SNR settings improves MSA performance, particularly in static cases, whereas SSM performance is less influenced by training set diversification.
MIMO Scenarios
For MIMO setups, where complexity and dimensionality are increased:
- In-distinction performance: MSA consistently outperforms SSM in capturing the spatial correlations needed for effective MIMO CSI prediction.
- Generalization: Both architectures struggle with highly diverse MIMO environments, indicating room for further model improvements.
Conclusion
The research demonstrates that while MSA layers show advantages in handling MIMO scenarios given their ability to learn intricate patterns and relationships within data, SSM layers offer efficient solutions for environments with high user mobility. Future avenues include exploring hybrid models and extending benchmarks across more varied scenarios and channel characteristics to further push the boundaries of AI in 5G communications.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.