Nomadic Relay-Directed Joint Power and Subchannel Allocation in OFDMA-Based Cellular Fixed Relay Networks
Bibliographic record
Abstract
Various standardization activities leading to 4G and beyond networks have considered the synergy of OFDMA and multihop relaying thus giving way for the fixed relay station (FRS)-based radio access network. As such, the next-generation networks will comprise a plethora of performance enhancing devices among which is the plug-and-play nomadic relay station (NRS). It is essential for such dense and inevitably frequency reuse-aggressive networks to employ efficient mechanisms to mitigate the co-channel interference and to provide prudent energy utilization; this brings about the timely environmental concerns and the so-called green wireless initiative in designing future wireless networks. We present a novel joint power and subchannel allocation algorithm for the emerging OFDMA-based nomadic-augmented fixed relay networks. This NRS-directed algorithm performs adaptive power control (APC) within the autonomous opportunistic NRS medium access and channel reuse, using two different approaches. The APC mechanism is realized in an open-loop manner requiring no feedback from the WT. We demonstrate the power savings and user throughput improvement obtained through the joint scheme. We also identify a throughput-power saving trade-off in terms of the number of deployed FRSs. Through this work, the authors further establish their pioneering techniques for realizing the concept of NRS-augmented networks.
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 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 | 0.002 |
| 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 | 0.001 |
| 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 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".