Deep Reinforcement Learning-based Sum-Rate Maximization in Hybrid Beamforming Multi-User Massive MIMO Systems
Bibliographic record
Abstract
This work develops deep reinforcement learning (RL)-based techniques to perform power allocation in hybrid beamforming (HB) multi-user massive multiple-input multiple-output (MU-mMIMO) systems. We aim to maximize the achievable system sum-rate constrained to a total transmit power limit. The optimization problem is non-convex and solved in three main steps. First, a radio frequency (RF) beamformer is designed based on the slow time-varying angle of departure (AoD) information of users. Second, a baseband (BB) precoder is derived based on regularized zero-forcing technique using the lower-dimensional effective channel state information (CSI) seen from the BB stage. Afterward, the power allocation (PA) task is formulated in the reinforcement learning framework and it is solved by two main approaches: (i) single-agent deep Q-learning (SA-DQL), (ii) multi-agent deep Q-learning (MA-DQL). An RL agent learns to choose an action (i.e., PA block/value) that maximizes its expected cumulative reward (i.e., sum-rate) through interactions with the environment. It is shown that SA-DQL-based PA can closely approach the performance of exhaustive search in terms of achieved sum-rate and performs significantly better than the MA-DQL-based PA and equal PA scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".