A coalitional game‐based relay load balancing and power allocation scheme in decode‐and‐forward cellular relay networks
Bibliographic record
Abstract
Abstract In this paper, a game theoretic relay load balancing and power allocation scheme is proposed for downlink transmission in a decode‐and‐forward orthogonal frequency division multiple access‐based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game‐based scheme can considerably improve the average sum‐spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".