On the Performance and Power Optimization of Multihop Multibranch Relaying Networks With Cochannel Interferers
Bibliographic record
Abstract
In this paper, we study the performance of opportunistic amplify-and-forward (AF) multihop multibranch relaying networks operating in the presence of cochannel interference (CCI). We obtain exact and upper bound expressions for the end-to-end signal-to-interference-plus-noise ratio (SINR), assuming transmissions over independent nonidentical Rayleigh fading channels. Afterward, the cumulative distribution function (cdf) and the probability density function (pdf) of the upper bound end-to-end SINR are investigated. According to these statistics, we obtain a lower bound closed-form expression for the outage probability. Furthermore, an approximate expression for the pdf of the end-to-end SINR is derived. Subsequently, simple expressions for the approximate error and outage probabilities are provided. These expressions deliver more understanding on the effect of the system parameters. Moreover, we address the optimization of the power allocation among the transmit nodes to enhance the overall system performance. As an optimal solution for the resource-allocation problem at hand, the adaptive power allocation minimizes the error probability under constraint on the aggregate power over the branch with the maximum SINR. It is shown that by applying the energy obtained through the optimization process, the performance of the network is improved significantly. Eventually, the accuracy of the analysis is validated by comparing the numerical results with Monte Carlo simulations, and insightful discussions are provided.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".