ADC Bit Allocation for massive MIMO using modified dynamic programming
Bibliographic record
Abstract
An optimal ADC Bit-Allocation (BA) Algorithm based on the maximization of the cost$\mathrm{K}_{f}$was derived in [1], [2]. The optimal BA ensures Minimum Mean Squared Error performance of the massive Multiple-Input Multiple-Output receiver. However, this Algorithm has an additive complexity of of$O(N_{b}^{N_{s}})$, where$N_{b}$is the ADC bit range and$N_{s}$the number of RF paths. In this paper, we propose a modified dynamic programming algorithm that significantly reduces the additive complexity to$O(N_{b}^{2}N_{s}N_{e}^{\prime})$. Typically, dynamic programming is used to solve optimization problems with linear constraints, given the cost function satisfies the principle of optimality. Here, we modify the cost$\mathrm{K}_{f}$as a multi-valued function and show that it satisfies the principle of optimality under a power constraint, which is non-linear. This results in maintaining multiple winning paths (survivors) at each node in the trellis to arrive at the optimal BA solution. We also derive the minimum number of survivors$N_{e}^{\prime}$required to do so. Using Monte Carlo simulations, we compare the MSE performance of the proposed algorithm with that of the algorithm in [1].
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 0.001 |
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".