End-to-end intelligent MCS selection algorithm

In this paper, an end-to-end intelligent modulation and coding scheme (MCS) algorithm is proposed, where the reinforcement learning (RL) is exploited. The proposed algorithm takes channel information and measurements as the input, and intelligently selects the proper MCS value to obtain the maximum...

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Bibliographic Details
Main Authors: Xue, Xiaosong, Pan, Wei, Yan, Fang, Li, Na
Format: Conference Proceeding
Language:English
Subjects:
Deep Q-network
End-to-end
Forestry
Interference
MCS selection
Modulation
Reinforcement learning
Simulation
Technological innovation
Throughput
Online Access:Request full text
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Summary:In this paper, an end-to-end intelligent modulation and coding scheme (MCS) algorithm is proposed, where the reinforcement learning (RL) is exploited. The proposed algorithm takes channel information and measurements as the input, and intelligently selects the proper MCS value to obtain the maximum throughput. The proposed is end-to-end and the simulation results show the better performance of the proposed algorithm. Average MCS values and corresponding throughput are respectively improved by 2.1% and 2%.
ISSN:2768-1904
DOI:10.1109/CSCWD61410.2024.10580386