Performance Analysis of Full-Duplex Relaying Employing Fiber-Connected Distributed Antennas
Bibliographic record
Abstract
Different from the previous studies on distributed-antenna (DA) systems (DASs) that mostly focused on the enhancement of downlink or uplink performance of cellular systems through cooperation of DAs, this paper proposes and investigates the performance of a novel fiber-connected DA relay system (DARS) that employs radio-over-fiber (RoF) techniques to interconnect a DAS to a central processor, which collectively form a relay node. Under the assumption that self-interference can be perfectly canceled in DARS, full-duplex (FD) operation is possible, by which some antennas receive from the source, whereas other antennas simultaneously transmit to the destination. The numbers of transmit and receive antennas can be controlled to achieve a balance between the transmissions of the source-relay (SR) and relay-destination (RD) links. Consequently, higher spectral efficiency can be achieved compared with half-duplex (HD) relaying systems. For Nakagami fading channels with no direct source-destination (SD) link, we show that DARS can obtain the optimal diversity-multiplexing tradeoff (DMT). Moreover, as the primary purpose of FD relaying is to increase the throughput, we also analyze the throughput performance of DARS. Numerical results show that the FD-DARS with a large number of antennas exhibits a much better throughput performance than HD relaying systems and in regions with high signal-to-noise ratios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".