A Deep Learning Framework for Beam Selection and Power Control in Massive MIMO - Millimeter-Wave Communications
Bibliographic record
Abstract
A fine power control policy and beam alignment is required between the base station (BS) and user equipment (UE) to achieve the promising performance of massive multiple input multiple output (MIMO) in millimeter wave (mmWave) communications. However, obtaining the channel state information (CSI) of mmWave - massive MIMO systems is challenging. In this paper, the beam-steering technique is used to estimate the signal strength from the BS to the user. We propose a novel learning framework to determine the suitable beam for a specific user and the transmit power for minimizing the cost including the transmit power and the unsatisfied rate when the channel is unknown. In addition, we address the missing data problem, and then employ the long-short term memory (LSTM) on the temporal processed inputs to select the suitable beam. Furthermore, we design a learning agent to predict the proper transmit power from the transmitted SSBs taking into account the required transmission rate. We then validate the proposed learning framework on the Deep MIMO dataset constructed based on accurate ray-tracing channels. Numerical results show our proposed framework outperforms the state-of-the-art prediction strategies, and approximates the best performance which is obtained when the CSI is available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".