Uplink/Downlink Rate Analysis and Impact of Power Allocation for Full-Duplex Cloud-RANs
Bibliographic record
Abstract
This paper considers a cloud radio access network, where full-duplex (FD) users communicate with remote radio heads (RRHs) that are spatially distributed. We consider all participate RRH association (ARA) and single nearest RRH association (SRA) policies with optimal, maximum ratio combining/maximal ratio transmission (MRT), and zero-forcing/MRT (ZF/MRT) processing schemes and derive analytical expressions useful to compare the average uplink/downlink (UL/DL) sum rate among association schemes as a function of the number of RRHs antennas and UL/DL RRH density. We also study a dense network setting with multiple FD users and derive exact expressions for the average UL/DL rates, where a user-centric clustering technique is adopted and each user is served by its nearest UL and DL RRHs. Furthermore, by maximizing the instantaneous sum rate, we develop an optimum power allocation scheme for the single-user case. We observe that ARA results in a rate region that is strongly biased toward the UL or DL, but using SRA results in a more balanced rate region. Moreover, SRA policy with ZF/MRT processing achieves up to 32% and 42% average sum rate gains as compared with the HD SRA and FD ARA counterparts, respectively.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".