Energy Efficient ADC Bit Allocation for Massive MIMO: A Deep-Learning Approach
Bibliographic record
Abstract
It is known that adopting Variable-Resolution (VR) ADCs in millimeter-wave (mmWave) Massive Multiple-Input Multiple-Output (MaMIMO) receivers improves Energy Efficiency (EE). However, the effect of imperfect channel state information (CSI) at the receiver is detrimental in achieving the EE. None of the previous works consider imperfect CSI for designing ADC Bit Allocation (BA) for MaMIMO receivers. We propose a deep learning based framework to achieve a near-optimal EE for MaMIMO receivers. The contributions of this paper include a machine learning approach to arrive at a BA that achieves near-optimal EE by training the framework for a combination of perfect and imperfect channels using the conditions derived for capacity maximization. Using simulations, we show that the EE obtained using our proposed approach is very close to that of the brute force both for perfect and imperfect channels. Also, through simulations, we claim a computational complexity advantage using the proposed approach compared to brute force after sufficient learning of the channels presented to the system.
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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 | 0.001 |
| 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".