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Deep Learning-Based Modulation and Power Control: A BER Perspective
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DOI：10.1109/LCOMM.2025.3605917.png)
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Abstract
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This letter presents a deep learning (DL)-based modulation and power control (MPC) framework for two bit error rate (BER)-based optimization problems under parallel fading channels. First, we aim to minimize BER by designing two neural networks, ModNet and PowerNet, which optimize modulation and power levels, respectively, while satisfying their constraints. Second, we seek to minimize total power while ensuring a target BER by training ModNet for modulation level decision, followed by a convex optimization process for power allocation. Simulation results validate the effectiveness of the proposed DL-based MPC framework, demonstrating lower BER, reduced total power consumption, and faster simulation time compared to existing MPC baselines.
Keywords:
Modulation and power control
deep learning
bit error rate
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