Hybrid ARQ in Multicell MU-SIMO With Large-Scale Antenna Arrays
Bibliographic record
Abstract
We consider hybrid automatic repeat and request (HARQ) schemes in the uplink of a multicell multiuser single-input multiple-output system with large-scale antenna arrays at the base station (BS) for improving the spectral efficiency while limiting the number of retransmissions. Assuming a zero-forcing receiver at each BS and that each BS knows only the channel gains of users in its own cell, we derive the expressions for the outage probability and long-term average transmission rate (LATR) of the Type-I HARQ, HARQ with chase-combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). We then formulate the optimal rate-selection problems for the three schemes and provide methods to find a solution. Since the optimal-rate selection methods for the HARQ-CC and HARQ-IR schemes are computationally intensive, we propose sub-optimal rate-selection methods, which yield a closer LATR performance to that of the optimal methods. For the Type-I HARQ and HARQ-IR, we also present a parameterization-based method showing the approximate relation between the expressions of the optimized-LATR, optimal rate, and number of antennas. Illustrative results show that the HARQ-IR has significant gain over HARQ-CC and Type-I HARQ in terms of LATR, while Type-I HARQ yields practically similar performance to HARQ-CC as the number of antennas at the BS increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".