Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917897163505664 |
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| author | Celebi, Hasan Basri Skoglund, Mikael |
| author_facet | Celebi, Hasan Basri Skoglund, Mikael |
| contents | This paper presents a comprehensive system model for goodput maximization with quantized feedback in Ultra-Reliable Low-Latency Communication (URLLC), focusing on dynamic channel conditions and feedback schemes. The study investigates a communication system, where the receiver provides quantized channel state information to the transmitter. The system adapts its feedback scheme based on reinforcement learning, aiming to maximize goodput while accommodating varying channel statistics. We introduce a novel Rician-$K$ factor estimation technique to enable the communication system to optimize the feedback scheme. This dynamic approach increases the overall performance, making it well-suited for practical URLLC applications where channel statistics vary over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC Celebi, Hasan Basri Skoglund, Mikael Information Theory Machine Learning Signal Processing This paper presents a comprehensive system model for goodput maximization with quantized feedback in Ultra-Reliable Low-Latency Communication (URLLC), focusing on dynamic channel conditions and feedback schemes. The study investigates a communication system, where the receiver provides quantized channel state information to the transmitter. The system adapts its feedback scheme based on reinforcement learning, aiming to maximize goodput while accommodating varying channel statistics. We introduce a novel Rician-$K$ factor estimation technique to enable the communication system to optimize the feedback scheme. This dynamic approach increases the overall performance, making it well-suited for practical URLLC applications where channel statistics vary over time. |
| title | Reinforcement Learning Based Goodput Maximization with Quantized Feedback in URLLC |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.11190 |