TY - JOUR
AU - Qi, Lin
AB - At present, artificial intelligence and big data are flourishing, driving wireless communication services to a more efficient and intelligent direction, communication devices are increasing dramatically, and the communication environment is becoming increasingly complex, so the decision-making link is crucial to ensure communication performance as much as possible. To address the problems that existing waveform parameter decision algorithms rely on high a priori knowledge, lack of compatibility, and low decision efficiency, a reinforcement learning-based waveform parameter decision method is proposed. The method introduces a dynamic ε mechanism based on the hill-climbing strategy (PHC) under the architecture of the reinforcement learning algorithm and proposes a dynamic ε Q-learning intelligent decision algorithm, which enables the decision model to select ε values more optimally according to the state of the decision network and improves the convergence speed and decision success rate. The algorithm makes full use of the interaction between reinforcement learning and the environment and generates waveform parameter combinations which are suitable for the current channel environment in real-time through online learning. The decision model is based on a multi-carrier spread spectrum (MC-SS) communication system. The simulation results show that the new decision algorithm does not rely on a priori knowledge and has higher decision efficiency, which not only gives suitable decision results in Gaussian channels but also adapts to various fading channels and outperforms the mapping results provided by the Modulation and Coding Scheme (MCS) index table.
TI - Communication waveform parameter decision based on reinforcement learning
JO - Proceedings of SPIE
DO - 10.1117/12.2673795
DA - 2023-04-06
UR - https://www.deepdyve.com/lp/spie/communication-waveform-parameter-decision-based-on-reinforcement-Lbwph1kL33
SP - 126151M
EP - 126151M-6
VL - 12615
IS - 
DP - DeepDyve
ER -