Deep Reinforcement Learning Based Link Adaptation Technique for LTE/NR Systems

Ye, Xiaowen; Yu, Yiding; Fu, Liqun

doi:10.1109/tvt.2023.3236791

Cited by 10 publications

(1 citation statement)

References 38 publications

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“…Although not discussing vehicular networks in particular, the reports examine the general idea of adapting the AMC in the MIMO and 5G-MIMO systems, respectively. The use of CQI information to construct the AMC mechanism is presented in some reports, such as in [17][18][19][20][21]. From our interpretation, despite the majority of the reports posing a real-world configuration, most of the following reports still need to present a discussion of the overall packet received ratio (PRR) performance.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

Bayu,

Huang,

Chen

2023

Future Internet

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The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3rd Generation Partnership Project (3GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors’ knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.

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Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

Bayu,

Huang,

Chen

2023

Future Internet

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Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

Sun,

Yan,

Liang

et al. 2024

Aerospace Science and Technology

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ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecondlevel delays and very high reliability, i.e., less than 10 −5 packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.

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Deep Reinforcement Learning Based Link Adaptation Technique for LTE/NR Systems

Cited by 10 publications

References 38 publications

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

Memory-extraction-based DRL cooperative guidance against the maneuvering target protected by interceptors

ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

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