6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915508745404416 |
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| author | Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. |
| author_facet | Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. |
| contents | This work introduces 6G Twin, the first end-to-end artificial intelligence (AI)-native radio access network (RAN) design that unifies (i) neural Gaussian Radio Fields (GRF) for compressed channel state information (CSI) acquisition, (ii) continual channel prediction with handover persistence, and (iii) an energy-optimal nonlinear precoder (minPMAC). GRF replaces dense pilots with a sparse Gaussian field, cutting pilot overhead by about 100x while delivering 1.1 ms inference and less than 2 minutes on-site training, thus enabling millisecond-scale closed-loop operation. A replay-driven continual learner sustains accuracy under mobility and cell transitions, improving channel normalized mean square error (NMSE) by more than 10 dB over frozen predictors and an additional 2-5 dB over uniform replay, thereby stabilizing performance across UMi/UMa handovers. Finally, minPMAC solves a convex, order-free MAC precoder design that recovers the globally optimal order from Broadcast Channel (BC) duals and minimizes transmit energy subject to minimum-rate guarantees, achieving 4-10 times lower energy (scenario dependent) with monotonically increasing bits per joule as SNR grows. This translates to up to 5 times higher data rate at comparable power or the same rates at substantially lower power. Together, these components form a practical, GPU-ready framework that attains real-time CSI, robust tracking in dynamic networks with efficient handovers, and state-of-the-art throughput-energy tradeoffs under 3GPP-style settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks Mohsin, Muhammad Ahmed Umer, Muhammad Bilal, Ahsan Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. Distributed, Parallel, and Cluster Computing This work introduces 6G Twin, the first end-to-end artificial intelligence (AI)-native radio access network (RAN) design that unifies (i) neural Gaussian Radio Fields (GRF) for compressed channel state information (CSI) acquisition, (ii) continual channel prediction with handover persistence, and (iii) an energy-optimal nonlinear precoder (minPMAC). GRF replaces dense pilots with a sparse Gaussian field, cutting pilot overhead by about 100x while delivering 1.1 ms inference and less than 2 minutes on-site training, thus enabling millisecond-scale closed-loop operation. A replay-driven continual learner sustains accuracy under mobility and cell transitions, improving channel normalized mean square error (NMSE) by more than 10 dB over frozen predictors and an additional 2-5 dB over uniform replay, thereby stabilizing performance across UMi/UMa handovers. Finally, minPMAC solves a convex, order-free MAC precoder design that recovers the globally optimal order from Broadcast Channel (BC) duals and minimizes transmit energy subject to minimum-rate guarantees, achieving 4-10 times lower energy (scenario dependent) with monotonically increasing bits per joule as SNR grows. This translates to up to 5 times higher data rate at comparable power or the same rates at substantially lower power. Together, these components form a practical, GPU-ready framework that attains real-time CSI, robust tracking in dynamic networks with efficient handovers, and state-of-the-art throughput-energy tradeoffs under 3GPP-style settings. |
| title | 6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.18735 |