An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909732051091456 |
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| author | Rezazadeh, Farhad Zhao, Raymond Dai, Jiongyu Gargari, Amir Ashtari Chergui, Hatim Liu, Lingjia |
| author_facet | Rezazadeh, Farhad Zhao, Raymond Dai, Jiongyu Gargari, Amir Ashtari Chergui, Hatim Liu, Lingjia |
| contents | Next-generation open radio access networks (O-RAN) continuously stream tens of key performance indicators (KPIs) together with raw in-phase/quadrature (IQ) samples, yielding ultra-high-dimensional, non-stationary time series that overwhelm conventional transformer architectures. We introduce a reservoir-augmented masked autoencoding transformer (RA-MAT). This time series foundation model employs echo state network (ESN) computing with masked autoencoding to satisfy the stringent latency, energy efficiency, and scalability requirements of 6G O-RAN testing. A fixed, randomly initialized ESN rapidly projects each temporal patch into a rich dynamical embedding without backpropagation through time, converting the quadratic self-attention bottleneck into a lightweight linear operation. These embeddings drive a patch-wise masked autoencoder that reconstructs 30% randomly masked patches, compelling the encoder to capture both local dynamics and long-range structure from unlabeled data. After self-supervised pre-training, RA-MAT is fine-tuned with a shallow task head while keeping the reservoir and most transformer layers frozen, enabling low-footprint adaptation to diverse downstream tasks such as O-RAN KPI forecasting. In a comprehensive O-RAN KPI case study, RA-MAT achieved sub-0.06 mean squared error (MSE) on several continuous and discrete KPIs. This work positions RA-MAT as a practical pathway toward real-time, foundation-level analytics in future 6G networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study Rezazadeh, Farhad Zhao, Raymond Dai, Jiongyu Gargari, Amir Ashtari Chergui, Hatim Liu, Lingjia Networking and Internet Architecture Next-generation open radio access networks (O-RAN) continuously stream tens of key performance indicators (KPIs) together with raw in-phase/quadrature (IQ) samples, yielding ultra-high-dimensional, non-stationary time series that overwhelm conventional transformer architectures. We introduce a reservoir-augmented masked autoencoding transformer (RA-MAT). This time series foundation model employs echo state network (ESN) computing with masked autoencoding to satisfy the stringent latency, energy efficiency, and scalability requirements of 6G O-RAN testing. A fixed, randomly initialized ESN rapidly projects each temporal patch into a rich dynamical embedding without backpropagation through time, converting the quadratic self-attention bottleneck into a lightweight linear operation. These embeddings drive a patch-wise masked autoencoder that reconstructs 30% randomly masked patches, compelling the encoder to capture both local dynamics and long-range structure from unlabeled data. After self-supervised pre-training, RA-MAT is fine-tuned with a shallow task head while keeping the reservoir and most transformer layers frozen, enabling low-footprint adaptation to diverse downstream tasks such as O-RAN KPI forecasting. In a comprehensive O-RAN KPI case study, RA-MAT achieved sub-0.06 mean squared error (MSE) on several continuous and discrete KPIs. This work positions RA-MAT as a practical pathway toward real-time, foundation-level analytics in future 6G networks. |
| title | An Experimental Reservoir-Augmented Foundation Model: 6G O-RAN Case Study |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.07778 |