Bài báo quốc tế
Q-Learning for Adaptive MMSE Regularization in 1-Bit Massive MIMO
Đặng Ngọc Hùng
This paper develops a Q-Learning framework for adaptive MMSE regularization in massive MIMO systems with 1-bit ADCs. The receiver adjusts its regularization parameter to instantaneous channel conditions via a tabular agent operating on a 6-dimensional state and 75 discrete actions. Evaluation on 20,000 independent test samples shows consistent gains: 1.27–1.70% over the best Bussgang-MMSE baseline and up to 47%
over MRC across antenna settings N ∈ {16, 32, 64, 96, 128}. Training exhibits stable learning with the positive-reward rate rising from 16.1% to 49.8% over 20,000 episodes. This gain is achieved with negligible inference overhead, adding only an O(1) table lookup to the standard O(N3) MMSE complexity.
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