A POMDP-Based Antenna Selection for Massive MIMO Communication
Bibliographic record
Abstract
We use a partially observable Markov decision process (POMDP) framework to design an optimal antenna selection policy for downlink transmit beamforming at a multi-antenna base station (BS) equipped with only a limited number of RF chains. Assuming that the channel state evolves according to a finite-state Markov process and that only the channel coefficients which correspond to previously selected antennas, are available at the BS, we use the POMDP framework for antenna selection with the aim to maximize thelong-term expected downlink data rate. To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy and prove thatin the case of positively correlated two-state Markov model for the channel over each antenna, the myopic policy is optimal for antenna selection for any number of RF chains. Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-state coarse channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which uses full channel state information without quantization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".