Rate Maximization Based Power Allocation and Relay Selection With IRI Consideration for Two-Path AF Relaying
Bibliographic record
Abstract
We consider the power allocation and relay selection for rate maximization in a two-path amplify-and-forward (AF) relay network with inter-relay interference (IRI) consideration. We first investigate the power allocation with only a pair of relays under both the individual and global power constraints. To find the global optimum solution to this nonconvex problem, we develop a three-step approach using the rate-profiling technique together with a reformulation of the sum-rate maximization problem as a set of power-minimization geometric programming problems (GPPs). For reduced complexity, we further convert the optimization problem into a set of GPPs in a single-step by using a high signal-to-interference-plus-noise ratio approximation. Next, we consider the relay pair selection and propose an algorithm in which the achievable rate of each pair of relays with the proposed power allocation is compared. This selection criterion outperforms the conventional selection scheme in terms of the achievable rate. We further propose two low-complexity selection criteria for low and moderate IRI. For moderate IRI, the ratio of the source-relay and relay-destination channel power product to the square of inter-relay channel power can be used for relay selection to achieve a performance close to that of the selection based on the proposed power allocation.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".