Adaptive CDI-CQI Feedback Bit Partitioning for Quantized MISO-SDMA in Downlink HetNets
Bibliographic record
Abstract
In this paper we analyze and design a Heterogenous cellular networks (HetNets) where base stations (BSs) are equipped with multi-antennas, serving a number of singleantenna user equipments (UEs) in downlink. We focus of space division multiple access (SDMA) whereby (i) zero-forcing beamforming vectors at the BSs are chosen based on the quantized channel direction information (CDI) and (ii) scheduled data rates are specified adaptively based on the quantized channel quality information (CQI). Adopting tools of stochastic geometry, we firstly, provide a tight approximation of the coverage performance of quantized SDMA beamforming in a closed-form that encompasses impacts of feedback capacity, density of BSs, and SIR thresholds under max-SIR cell association rule. Assuming CQI of each UE is its experienced SIR, we then exploit our analysis to devise a proper optimization problem to adaptively share feedback capacity between tiers, and also between CDI and CQI feedback. We do so by introducing net spatial throughput (NST) as the main performance metric, which is defined as the spatial throughput minus uplink penalty of feedback capacity per area per frame. Our numerical results show that serving more UEs calls for higher capacity of feedback channel without delivering strong NST. We further observe that one way to coup with such a weak performance is to densify the network in tier 2 wherever path-loss exponent is adequately large, e.g., installing many femto-cells in indoor.
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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".